U-Net.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# U-Net图像分割\n",
    "\n",
    "code来源：\n",
    "https://www.kaggle.com/keegil/keras-u-net-starter-lb-0-277?scriptVersionId=2164855/notebook\n",
    "\n",
    "数据来源code：\n",
    "https://github.com/kamalkraj/DATA-SCIENCE-BOWL-2018/blob/master/Data_Science_Bowl_2018.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data download and unzipping\n",
    "!wget https://raw.githubusercontent.com/AakashSudhakar/2018-data-science-bowl/master/compressed_files/stage1_test.zip -c\n",
    "!wget https://raw.githubusercontent.com/AakashSudhakar/2018-data-science-bowl/master/compressed_files/stage1_train.zip -c\n",
    "\n",
    "!mkdir stage1_train stage1_test\n",
    "!unzip stage1_train.zip -d stage1_train/\n",
    "!unzip stage1_test.zip -d stage1_test/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import random\n",
    "import warnings\n",
    "\n",
    "# 需要指定一下数据目录\n",
    "sys.path.append('/mnt/unet/')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "import tensorflow as tf\n",
    "from keras.backend.tensorflow_backend import set_session\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.per_process_gpu_memory_fraction = 0.4\n",
    "set_session(tf.Session(config=config))\n",
    "\n",
    "from tqdm import tqdm\n",
    "from itertools import chain\n",
    "from skimage.io import imread, imshow, imread_collection, concatenate_images\n",
    "from skimage.transform import resize\n",
    "from skimage.morphology import label\n",
    "\n",
    "from keras.models import Model, load_model\n",
    "from keras.layers import Input\n",
    "from keras.layers.core import Lambda\n",
    "from keras.layers.convolutional import Conv2D, Conv2DTranspose\n",
    "from keras.layers.pooling import MaxPooling2D\n",
    "from keras.layers.merge import concatenate\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from keras import backend as K\n",
    "\n",
    "# Set some parameters\n",
    "IMG_WIDTH = 128\n",
    "IMG_HEIGHT = 128\n",
    "IMG_CHANNELS = 3\n",
    "TRAIN_PATH = './stage1_train/'\n",
    "TEST_PATH = './stage1_test/'\n",
    "\n",
    "warnings.filterwarnings('ignore', category=UserWarning, module='skimage')\n",
    "seed = 42\n",
    "random.seed = seed\n",
    "np.random.seed = seed\n",
    "\n",
    "# Get train and test IDs\n",
    "train_ids = next(os.walk(TRAIN_PATH))[1]  # 670\n",
    "test_ids = next(os.walk(TEST_PATH))[1]    # 65"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the data\n",
    "Let's first import all the images and associated masks. I downsample both the training and test images to keep things light and manageable, but we need to keep a record of the original sizes of the test images to upsample our predicted masks and create correct run-length encodings later on. There are definitely better ways to handle this, but it works fine for now!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting and resizing train images and masks ... \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 670/670 [01:16<00:00,  8.74it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting and resizing test images ... \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████| 65/65 [00:00<00:00, 100.11it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Generate Done!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "def data_generator(train_ids,test_ids):\n",
    "    # Get and resize train images and masks\n",
    "    X_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)\n",
    "    Y_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool)\n",
    "    print('Getting and resizing train images and masks ... ')\n",
    "    sys.stdout.flush()\n",
    "    for n, id_ in tqdm(enumerate(train_ids), total=len(train_ids)):\n",
    "        path = TRAIN_PATH + id_\n",
    "        img = imread(path + '/images/' + id_ + '.png')[:,:,:IMG_CHANNELS]\n",
    "        img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True) # resize成128*128\n",
    "        X_train[n] = img\n",
    "        mask = np.zeros((IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool)\n",
    "        for mask_file in next(os.walk(path + '/masks/'))[2]:\n",
    "            mask_ = imread(path + '/masks/' + mask_file)\n",
    "            mask_ = np.expand_dims(resize(mask_, (IMG_HEIGHT, IMG_WIDTH), mode='constant', \n",
    "                                          preserve_range=True), axis=-1)\n",
    "            mask = np.maximum(mask, mask_)\n",
    "        Y_train[n] = mask\n",
    "\n",
    "    # Get and resize test images\n",
    "    X_test = np.zeros((len(test_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)\n",
    "    sizes_test = []\n",
    "    print('Getting and resizing test images ... ')\n",
    "    sys.stdout.flush()\n",
    "    for n, id_ in tqdm(enumerate(test_ids), total=len(test_ids)):\n",
    "        path = TEST_PATH + id_\n",
    "        img = imread(path + '/images/' + id_ + '.png')[:,:,:IMG_CHANNELS]\n",
    "        sizes_test.append([img.shape[0], img.shape[1]])\n",
    "        img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n",
    "        X_test[n] = img\n",
    "\n",
    "    print('Data Generate Done!')\n",
    "    return X_train,Y_train,X_test,sizes_test\n",
    "\n",
    "X_train,Y_train,X_test,sizes_test = data_generator(train_ids,test_ids)\n",
    "# X_train (670, 128, 128, 3)\n",
    "# Y_train (670, 128, 128, 1)\n",
    "# X_test  (65, 128, 128, 3)\n",
    "# sizes_test (65,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P0KbDVZT4dCQ+p9B2qm5s2/f5xBjmXIWOhchZEaHtWlkxy1eSl1Z6PrHjKRk+Laxk6cqP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\nFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f53b9a97438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rgceq6umq+hlwN6N2nIU2W3S6Nmr/mVgyw/LbhoCcWl2zEEJfA3YOVy3OYTTwta+jkIxm\ncrwVOFRVH12yaR+we3i+m9FY0ZqpqpuqantVXcCofb5cVW8D7geu7aprqO1J4IkkLx9WXc5o8svW\nNmN0GnZJkucP/66LdbW32RKna6N9wNuHq2SXAM8sOW2bujWfYXmtBuWWGRi7itEo/H8CH2ys47WM\nusQPAN8cvq5iNP6yH3gU+FfgvMYaLwPuHZ6/dPhPcBj4e+C5TTX9HrAwtNs/Aptnoc2APwe+DTwI\n/B2jWYNb2gy4g9HY1M8Y9R6vP10bMbro8Mnh5+FbjK7wrWVdhxmN/Sz+DPz1kv0/ONT1CHDlatTg\nxzYktZqF0zFJZzFDSFIrQ0hSK0NIUitDSFIrQ0hSK0NIUqv/A5BmdE67EzhOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f53b9b20470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check if training data looks all right\n",
    "# 查看数据是否正确\n",
    "ix = random.randint(0, len(train_ids))\n",
    "imshow(X_train[ix])\n",
    "plt.show()\n",
    "imshow(np.squeeze(Y_train[ix]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create our Keras metric\n",
    "Now we try to define the mean average precision at different intersection over union (IoU) thresholds metric in Keras. TensorFlow has a mean IoU metric, but it doesn't have any native support for the mean over multiple thresholds, so I tried to implement this. I'm by no means certain that this implementation is correct, though! Any assistance in verifying this would be most welcome!\n",
    "\n",
    "Update: This implementation is most definitely not correct due to the very large discrepancy between the results reported here and the LB results. It also seems to just increase over time no matter what when you train ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define IoU metric\n",
    "def mean_iou(y_true, y_pred):\n",
    "    prec = []\n",
    "    for t in np.arange(0.5, 1.0, 0.05):\n",
    "        y_pred_ = tf.to_int32(y_pred > t)\n",
    "        score, up_opt = tf.metrics.mean_iou(y_true, y_pred_, 2)\n",
    "        K.get_session().run(tf.local_variables_initializer())\n",
    "        with tf.control_dependencies([up_opt]):\n",
    "            score = tf.identity(score)\n",
    "        prec.append(score)\n",
    "    return K.mean(K.stack(prec), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build and train our neural network\n",
    "\n",
    "Next we build our U-Net model, loosely based on U-Net: Convolutional Networks for Biomedical Image Segmentation and very similar to this repo from the Kaggle Ultrasound Nerve Segmentation competition.\n",
    "\n",
    "![模型结构图](https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/u-net-architecture.png)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def UNetModel(IMG_HEIGHT,IMG_WIDTH,IMG_CHANNELS):\n",
    "    inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))\n",
    "    s = Lambda(lambda x: x / 255) (inputs)\n",
    "\n",
    "    c1 = Conv2D(8, (3, 3), activation='relu', padding='same') (s)\n",
    "    c1 = Conv2D(8, (3, 3), activation='relu', padding='same') (c1)\n",
    "    p1 = MaxPooling2D((2, 2)) (c1)\n",
    "\n",
    "    c2 = Conv2D(16, (3, 3), activation='relu', padding='same') (p1)\n",
    "    c2 = Conv2D(16, (3, 3), activation='relu', padding='same') (c2)\n",
    "    p2 = MaxPooling2D((2, 2)) (c2)\n",
    "\n",
    "    c3 = Conv2D(32, (3, 3), activation='relu', padding='same') (p2)\n",
    "    c3 = Conv2D(32, (3, 3), activation='relu', padding='same') (c3)\n",
    "    p3 = MaxPooling2D((2, 2)) (c3)\n",
    "\n",
    "    c4 = Conv2D(64, (3, 3), activation='relu', padding='same') (p3)\n",
    "    c4 = Conv2D(64, (3, 3), activation='relu', padding='same') (c4)\n",
    "    p4 = MaxPooling2D(pool_size=(2, 2)) (c4)\n",
    "\n",
    "    c5 = Conv2D(128, (3, 3), activation='relu', padding='same') (p4)\n",
    "    c5 = Conv2D(128, (3, 3), activation='relu', padding='same') (c5)\n",
    "\n",
    "    u6 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (c5)\n",
    "    u6 = concatenate([u6, c4])\n",
    "    c6 = Conv2D(64, (3, 3), activation='relu', padding='same') (u6)\n",
    "    c6 = Conv2D(64, (3, 3), activation='relu', padding='same') (c6)\n",
    "\n",
    "    u7 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same') (c6)\n",
    "    u7 = concatenate([u7, c3])\n",
    "    c7 = Conv2D(32, (3, 3), activation='relu', padding='same') (u7)\n",
    "    c7 = Conv2D(32, (3, 3), activation='relu', padding='same') (c7)\n",
    "\n",
    "    u8 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same') (c7)\n",
    "    u8 = concatenate([u8, c2])\n",
    "    c8 = Conv2D(16, (3, 3), activation='relu', padding='same') (u8)\n",
    "    c8 = Conv2D(16, (3, 3), activation='relu', padding='same') (c8)\n",
    "\n",
    "    u9 = Conv2DTranspose(8, (2, 2), strides=(2, 2), padding='same') (c8)\n",
    "    u9 = concatenate([u9, c1], axis=3)\n",
    "    c9 = Conv2D(8, (3, 3), activation='relu', padding='same') (u9)\n",
    "    c9 = Conv2D(8, (3, 3), activation='relu', padding='same') (c9)\n",
    "\n",
    "    outputs = Conv2D(1, (1, 1), activation='sigmoid') (c9)\n",
    "\n",
    "    model = Model(inputs=[inputs], outputs=[outputs])\n",
    "    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=[mean_iou])\n",
    "    return model\n",
    "\n",
    "model = UNetModel(IMG_HEIGHT,IMG_WIDTH,IMG_CHANNELS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit model\n",
    "\n",
    "I'll just train for 10 epochs, which takes around 10 minutes in the Kaggle kernel with the current parameters.\n",
    "\n",
    "Update: Added early stopping and checkpointing and increased to 30 epochs.\n",
    "\n",
    "ou should easily be able to stabilize and improve the results just by changing a few parameters, tweaking the architecture a little bit and training longer with early stopping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 603 samples, validate on 67 samples\n",
      "Epoch 1/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.5000 - mean_iou: 0.4169Epoch 00001: val_loss improved from inf to 0.39077, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 6s 9ms/step - loss: 0.4996 - mean_iou: 0.4169 - val_loss: 0.3908 - val_mean_iou: 0.4232\n",
      "Epoch 2/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.3269 - mean_iou: 0.4247Epoch 00002: val_loss improved from 0.39077 to 0.24166, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.3260 - mean_iou: 0.4247 - val_loss: 0.2417 - val_mean_iou: 0.4361\n",
      "Epoch 3/30\n",
      "592/603 [============================>.] - ETA: 0s - loss: 0.1859 - mean_iou: 0.4758Epoch 00003: val_loss improved from 0.24166 to 0.15848, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.1856 - mean_iou: 0.4765 - val_loss: 0.1585 - val_mean_iou: 0.5148\n",
      "Epoch 4/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.1387 - mean_iou: 0.5452Epoch 00004: val_loss improved from 0.15848 to 0.15135, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.1384 - mean_iou: 0.5454 - val_loss: 0.1513 - val_mean_iou: 0.5742\n",
      "Epoch 5/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.1201 - mean_iou: 0.5971Epoch 00005: val_loss improved from 0.15135 to 0.12357, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.1200 - mean_iou: 0.5971 - val_loss: 0.1236 - val_mean_iou: 0.6175\n",
      "Epoch 6/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.1133 - mean_iou: 0.6338Epoch 00006: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.1130 - mean_iou: 0.6339 - val_loss: 0.1280 - val_mean_iou: 0.6477\n",
      "Epoch 7/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.1074 - mean_iou: 0.6593Epoch 00007: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.1072 - mean_iou: 0.6594 - val_loss: 0.1258 - val_mean_iou: 0.6702\n",
      "Epoch 8/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.1071 - mean_iou: 0.6795Epoch 00008: val_loss improved from 0.12357 to 0.11764, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.1067 - mean_iou: 0.6795 - val_loss: 0.1176 - val_mean_iou: 0.6873\n",
      "Epoch 9/30\n",
      "592/603 [============================>.] - ETA: 0s - loss: 0.0975 - mean_iou: 0.6946Epoch 00009: val_loss improved from 0.11764 to 0.10682, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0975 - mean_iou: 0.6948 - val_loss: 0.1068 - val_mean_iou: 0.7022\n",
      "Epoch 10/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0960 - mean_iou: 0.7082Epoch 00010: val_loss improved from 0.10682 to 0.10215, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0967 - mean_iou: 0.7083 - val_loss: 0.1021 - val_mean_iou: 0.7137\n",
      "Epoch 11/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0908 - mean_iou: 0.7189Epoch 00011: val_loss improved from 0.10215 to 0.10012, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0904 - mean_iou: 0.7189 - val_loss: 0.1001 - val_mean_iou: 0.7242\n",
      "Epoch 12/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0895 - mean_iou: 0.7289Epoch 00012: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0894 - mean_iou: 0.7289 - val_loss: 0.1001 - val_mean_iou: 0.7334\n",
      "Epoch 13/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0877 - mean_iou: 0.7375Epoch 00013: val_loss improved from 0.10012 to 0.09696, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0879 - mean_iou: 0.7375 - val_loss: 0.0970 - val_mean_iou: 0.7413\n",
      "Epoch 14/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0860 - mean_iou: 0.7449Epoch 00014: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0862 - mean_iou: 0.7449 - val_loss: 0.0990 - val_mean_iou: 0.7482\n",
      "Epoch 15/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0847 - mean_iou: 0.7516Epoch 00015: val_loss improved from 0.09696 to 0.08973, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0844 - mean_iou: 0.7516 - val_loss: 0.0897 - val_mean_iou: 0.7545\n",
      "Epoch 16/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0809 - mean_iou: 0.7573Epoch 00016: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0808 - mean_iou: 0.7573 - val_loss: 0.0929 - val_mean_iou: 0.7601\n",
      "Epoch 17/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0833 - mean_iou: 0.7626Epoch 00017: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0832 - mean_iou: 0.7627 - val_loss: 0.0953 - val_mean_iou: 0.7649\n",
      "Epoch 18/30\n",
      "592/603 [============================>.] - ETA: 0s - loss: 0.0777 - mean_iou: 0.7672Epoch 00018: val_loss improved from 0.08973 to 0.08723, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0783 - mean_iou: 0.7673 - val_loss: 0.0872 - val_mean_iou: 0.7695\n",
      "Epoch 19/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0789 - mean_iou: 0.7715Epoch 00019: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0790 - mean_iou: 0.7716 - val_loss: 0.0910 - val_mean_iou: 0.7736\n",
      "Epoch 20/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0780 - mean_iou: 0.7756Epoch 00020: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0778 - mean_iou: 0.7756 - val_loss: 0.0875 - val_mean_iou: 0.7775\n",
      "Epoch 21/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0753 - mean_iou: 0.7794Epoch 00021: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0753 - mean_iou: 0.7794 - val_loss: 0.0892 - val_mean_iou: 0.7812\n",
      "Epoch 22/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0747 - mean_iou: 0.7830Epoch 00022: val_loss improved from 0.08723 to 0.08300, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0746 - mean_iou: 0.7830 - val_loss: 0.0830 - val_mean_iou: 0.7846\n",
      "Epoch 23/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0750 - mean_iou: 0.7862Epoch 00023: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0752 - mean_iou: 0.7862 - val_loss: 0.0951 - val_mean_iou: 0.7876\n",
      "Epoch 24/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0745 - mean_iou: 0.7889Epoch 00024: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0749 - mean_iou: 0.7889 - val_loss: 0.0864 - val_mean_iou: 0.7904\n",
      "Epoch 25/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0754 - mean_iou: 0.7916Epoch 00025: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0756 - mean_iou: 0.7916 - val_loss: 0.0871 - val_mean_iou: 0.7929\n",
      "Epoch 26/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0746 - mean_iou: 0.7941Epoch 00026: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0747 - mean_iou: 0.7941 - val_loss: 0.0912 - val_mean_iou: 0.7953\n",
      "Epoch 27/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0745 - mean_iou: 0.7965Epoch 00027: val_loss improved from 0.08300 to 0.08124, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0743 - mean_iou: 0.7965 - val_loss: 0.0812 - val_mean_iou: 0.7976\n",
      "Epoch 28/30\n",
      "592/603 [============================>.] - ETA: 0s - loss: 0.0715 - mean_iou: 0.7988Epoch 00028: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0716 - mean_iou: 0.7988 - val_loss: 0.0892 - val_mean_iou: 0.7999\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0753 - mean_iou: 0.8009Epoch 00029: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0754 - mean_iou: 0.8009 - val_loss: 0.0860 - val_mean_iou: 0.8018\n",
      "Epoch 30/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0729 - mean_iou: 0.8028Epoch 00030: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0728 - mean_iou: 0.8028 - val_loss: 0.0857 - val_mean_iou: 0.8037\n"
     ]
    }
   ],
   "source": [
    "def model_fit(X_train,Y_train,model_name,epochs,batch_size,validation_split):\n",
    "    earlystopper = EarlyStopping(patience=5, verbose=1)\n",
    "    checkpointer = ModelCheckpoint(model_name, verbose=1, save_best_only=True)\n",
    "    results = model.fit(X_train, Y_train, validation_split=validation_split, \n",
    "                        batch_size=batch_size, epochs=epochs, \n",
    "                        callbacks=[earlystopper, checkpointer])\n",
    "\n",
    "model_name = 'model-dsbowl2018-1.h5'\n",
    "epochs = 30\n",
    "batch_size = 8\n",
    "validation_split = 0.1\n",
    "model_fit(X_train,Y_train,model_name,epochs,batch_size,validation_split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 603 samples, validate on 67 samples\n",
      "Epoch 1/30\n",
      "592/603 [============================>.] - ETA: 0s - loss: 0.0714 - mean_iou: 0.8531Epoch 00001: val_loss improved from inf to 0.08364, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 5s 8ms/step - loss: 0.0710 - mean_iou: 0.8533 - val_loss: 0.0836 - val_mean_iou: 0.8633\n",
      "Epoch 2/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0710 - mean_iou: 0.8615Epoch 00002: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0710 - mean_iou: 0.8615 - val_loss: 0.0874 - val_mean_iou: 0.8617\n",
      "Epoch 3/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0719 - mean_iou: 0.8607Epoch 00003: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0721 - mean_iou: 0.8608 - val_loss: 0.0911 - val_mean_iou: 0.8612\n",
      "Epoch 4/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0715 - mean_iou: 0.8620Epoch 00004: val_loss improved from 0.08364 to 0.08225, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0713 - mean_iou: 0.8620 - val_loss: 0.0822 - val_mean_iou: 0.8617\n",
      "Epoch 5/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0693 - mean_iou: 0.8619Epoch 00005: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0694 - mean_iou: 0.8619 - val_loss: 0.0894 - val_mean_iou: 0.8618\n",
      "Epoch 6/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0701 - mean_iou: 0.8622 ETA: 0s - loss: 0.0695 - mean_iou: 0.Epoch 00006: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0703 - mean_iou: 0.8622 - val_loss: 0.0828 - val_mean_iou: 0.8621\n",
      "Epoch 7/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0702 - mean_iou: 0.8622Epoch 00007: val_loss improved from 0.08225 to 0.08217, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0700 - mean_iou: 0.8622 - val_loss: 0.0822 - val_mean_iou: 0.8624\n",
      "Epoch 8/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0688 - mean_iou: 0.8630Epoch 00008: val_loss improved from 0.08217 to 0.08216, saving model to model-dsbowl2018-1.h5\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0685 - mean_iou: 0.8630 - val_loss: 0.0822 - val_mean_iou: 0.8630\n",
      "Epoch 9/30\n",
      "592/603 [============================>.] - ETA: 0s - loss: 0.0682 - mean_iou: 0.8631Epoch 00009: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0678 - mean_iou: 0.8631 - val_loss: 0.0843 - val_mean_iou: 0.8633\n",
      "Epoch 10/30\n",
      "592/603 [============================>.] - ETA: 0s - loss: 0.0658 - mean_iou: 0.8636Epoch 00010: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0659 - mean_iou: 0.8636 - val_loss: 0.0864 - val_mean_iou: 0.8641\n",
      "Epoch 11/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0654 - mean_iou: 0.8646Epoch 00011: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0656 - mean_iou: 0.8646 - val_loss: 0.0873 - val_mean_iou: 0.8647\n",
      "Epoch 12/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0719 - mean_iou: 0.8641Epoch 00012: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0718 - mean_iou: 0.8641 - val_loss: 0.0833 - val_mean_iou: 0.8643\n",
      "Epoch 13/30\n",
      "600/603 [============================>.] - ETA: 0s - loss: 0.0673 - mean_iou: 0.8645Epoch 00013: val_loss did not improve\n",
      "603/603 [==============================] - 3s 5ms/step - loss: 0.0672 - mean_iou: 0.8645 - val_loss: 0.0882 - val_mean_iou: 0.8645\n",
      "Epoch 00013: early stopping\n"
     ]
    }
   ],
   "source": [
    "# Fit model\n",
    "earlystopper = EarlyStopping(patience=5, verbose=1)\n",
    "checkpointer = ModelCheckpoint('model-dsbowl2018-1.h5', verbose=1, save_best_only=True)\n",
    "results = model.fit(X_train, Y_train, validation_split=0.1, batch_size=8, epochs=30, \n",
    "                    callbacks=[earlystopper, checkpointer])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make predictions 预测\n",
    "Let's make predictions both on the test set, the val set and the train set (as a sanity check). Remember to load the best saved model if you've used early stopping and checkpointing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train (670, 128, 128, 3)\n",
    "# Y_train (670, 128, 128, 1)\n",
    "# X_test  (65, 128, 128, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "603/603 [==============================] - 1s 2ms/step\n",
      "67/67 [==============================] - 0s 747us/step\n",
      "65/65 [==============================] - 0s 737us/step\n"
     ]
    }
   ],
   "source": [
    "# Predict on train, val and test\n",
    "model = load_model('model-dsbowl2018-1.h5', custom_objects={'mean_iou': mean_iou})\n",
    "preds_train = model.predict(X_train[:int(X_train.shape[0]*0.9)], verbose=1) # (603, 128, 128, 1)\n",
    "preds_val = model.predict(X_train[int(X_train.shape[0]*0.9):], verbose=1)   # (67, 128, 128, 1)\n",
    "preds_test = model.predict(X_test, verbose=1)  # (65, 128, 128, 1)\n",
    "\n",
    "# Threshold predictions\n",
    "# 二值化\n",
    "preds_train_t = (preds_train > 0.5).astype(np.uint8)\n",
    "preds_val_t = (preds_val > 0.5).astype(np.uint8)\n",
    "preds_test_t = (preds_test > 0.5).astype(np.uint8)\n",
    "\n",
    "# Create list of upsampled test masks\n",
    "# 上采样，回到原来的尺寸\n",
    "preds_test_upsampled = []  # (65,)\n",
    "for i in range(len(preds_test)):\n",
    "    preds_test_upsampled.append(resize(np.squeeze(preds_test[i]), \n",
    "                                       (sizes_test[i][0], sizes_test[i][1]), # 回到原来的尺寸\n",
    "                                       mode='constant', preserve_range=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bJbRhQnMA5kIID02Ov4HxoPQcEW0EgMnfPS3qcDgc5ziqmVAIYTcR/YKIrgkh\nPAHgXQAem/y7BcCnJn/v0ZYpsYDmjC0xIKlsjfdAO9KzXazfzM3NAQDWrFkDYBq8yOXNzMwsBCVy\nvBCzp/379y8qs1bzsuSV2EkufbONtJ6dmhmYoQ2CKzGe5vHs7CyAsfeSvZOxvscbFDJj5Wcs9bMS\nk87ZqU0XI2VLW8+nhZHWeJqbaBus+NsAvkJEswCeAvCbGLOrrxPRbQB+DuCDLetwOBznMGgIovBo\nNAqrVq1aOJZsKjGhuAyrt8ISCxN/ooP/xvFCGzZsWHQMTPWheCtgZkTsYctpEpr7bxvH0cVzyJVV\n40Wyaj/8PFiT4+fCf9euXbtwPWZL3N7MiJih8t/Yaylt11O6Dyt7b+OBs64gSpDu49ChQ9tDCNdL\n5XjEtMPh6BWDeXfMsoZuno8jirWeEYtmofWkxbPnCy+8AGA6e7IGMRqNFjxpnJb/xlpQbSyGhslp\nZ9DS/VtnUkkHKTE6rb2x5nPhhRcCmEan81/2iJ133nkLZTMDjWOnWN9jBstR71LUswXattTUodVt\nLCuEtsw6B2dCDoejVwyGCQFyPI4ldqetNqHxBuRiMWI2w8c8y6bKlLxh0n2WtCwJ1vib0vOw1mXx\n0kj6E7c/sxhmQCtXrlyUnqPSubzzzz//NF2PNR9mrPF7gMeOHVuULnefKQaRu0ep70rlpM610UKb\n5zVpa+FMyOFw9IrBMKHUqCqta0uwziqlUV0bzyG93VyasXJl1LIUrR2auizspK0WUdLktDMvMyFm\nQPyXWQ2zGI4Nmp2dXbCHmQ0zn5jp1MTKWFhGqew2z1yyRdtXUufaetidCTkcjl4xGCZU0ns0jEhK\nIx1bmFhuZtZ6NZp5c+8kad8V08xo2jW8NBtaImOtTFSTzxqZy9c5Lohj0TgWi5nRkSNHFlgR60XM\nlvg5MLvKaUA5NG3uwkMl5dO2u5W9pDTSXNlWOBNyOBy9YjBMCFga7Ueqo2YtXOsdKN2XtWxL3VbW\nYdUNauqM01m8OtK3jJnhcHwWxwXlyjxx4sQiTxlw+pcP+C8zJIkNMIOamZkxaYUpOzW/CwsLL5WZ\na6PSxg+5FYsWzoQcDkevGBQTYrRhRHEZUjrL6G1db1s0E8lTwohZgMbzotW0pPQlm63PTOvtS0Fq\nK/7LXyh46aXxJ8753b34/oloQevhPLG3jCPamQlJaLaHxHSkvivpmak8kl3Scc2zrtWbnAk5HI5e\nMRgmZImr0Og1Vl0jNfJb42mkOkpl5a7n2kATYyKllWDx5lhjW0p259JrY3Tid/h437fm94OaYP0H\nmDId9oZx2fz9p9x23fF9lM4+3YQKAAAbD0lEQVRZ20RK10wrtZHU30q/vdqyJQxmEAJsbnTrDWuF\nXo37P3ddKquN2G1dSknnLHXULJWsbVVL5VOIwxv4dRl+6ZSDF3mgOXHixMIyNw5o5KVcc5smDTTL\nGO1mgZb+JA3m2rri8lJl+3LM4XCcExgME5qfnz/t850a0TV3zir6lWaVXNouAsM0yypNnbXuUUte\nzTJMelaaZ5mzxfqMY3GZPyDHwYvsjm8K07x048+5xh+XyyG2gcsj0n8gPkaXjKNtGc3lfVxGrg4t\nnAk5HI5eMRgmVBppU2Kzdca3rKulvBaGJl23so82YnjJDk1d8adKSnm17EpjtySESrM628sBh/wp\n3eaHy5i5xJ/TtQjQufvS2mkV80t1SWVqbUjpVtr70cKZkMPh6BWDZEKaGaELL4rGptKxllVZZo42\n2k6MttpDbJPFW2llRLk6S5qKlnnm2AF7wix54+ua87Uezxr9L9c2tYy62Q5d9MkUnAk5HI5eMRgm\n1IRGv6n1ElnihayzuEUXyaWprUuTNle31qYS89Dej7aNmtfbsKhUXan7br5wmkqjrTNnQ1xfKo20\nWUOcv2SHlhFZfkdWL6oWzoQcDkevGAwTKq2lUzOYNHtII74Un5Kaga1xQW11kRI0ZdfqNG1YmsSA\nYqZR4z2Ln71kv4Yd8LnYKyYxilydpedR21dLmqRVT8rlK0HrYbb2c2dCDoejVwyGCQH52dKyPtUw\nnNRxLn2prlpGVCrTiqXyWDTLbtNGUntrtIvcPeYi7HMozeBaxqO1PwXJS2ZFSr/U2mV9LqW2ahs3\n5EzI4XD0ikExIe2o3JwBNbN0M53Wc1KKgaldwzdtk+JQrHpS6r7a2hnbEtfV1M2sTEeqq/mMY8bD\nyEU35+yOodFUpDbTbEigZatWb1gNg5L6e+kduVpProRWTIiIfo+IHiWiR4joq0S0nIi2ENFDRLSD\niL5GRLOdWOpwOM5JVDMhItoE4HcAvC6EcJSIvg7gQwDeC+CzIYS7iejPANwG4PPKMgHoYkekMhha\nj0SqjrYeN82aWatFxJBYTMr+GNaZrAuNK9fe8TbMjNQH1nPaYc5eC7SeH0s6LVuPbbAwoK60uFyf\n0TzrWm2rrSa0DMAKIloGYCWAXQDeCeAbk+t3AfjnLetwOBznMKqZUAhhJxH9EYBnABwF8FcAtgPY\nF0Lgl3LmAGwylLnoOI5iZTT1Dm1ZVu9Nqg7rLJ+7ntIJrOt+KX+zjtoZKrahVKfEGOLrzTfYgekn\nVvk8P/NTp06dplNwmfzme/x2fw2LNHt0MiysTRyOlgGlGLrWfknXseiFVoaXQzUTIqJ1AG4GsAXA\nZQBWAbjJkP92ItpGRNva/kgcDsfZizbesV8B8LMQwvMAQETfAnADgLVEtGzChjYD2JnKHELYCmAr\nAIxGozApo1ihJbbGquOU4iFy9VtYVXysXbNrZ8Uaz4VVx6nRtnIzLTMe/vg8b9G8Zs0aAMD69esB\nAC+88AJefPFFANOPzPPb71Y2Urpfq+dQYgEW3UzSBXNlarx7uTo0HtzcsVYL1aKNJvQMgLcS0Uoa\nW/MuAI8B+B6AD0zS3ALgnhZ1OByOcxzUZilERH8I4F8COAngxwD+NcYa0N0A1k/O/asQwsulckaj\nUeAdEDL1AOgmLsLCtrRsJc4r1W25j1rvRsou7Vpd6/0ozY5xmlj7YeazadNYMrziiisAABdffDGA\nMQMCgOXLl+Ppp58GAMzNzQGY7nzBzIj/5mJcarQL7TPSMm4LYu+f9I5cKq/E0rU2NI+tmujBgwe3\nhxCul+pqFawYQvgkgE9Gp58C8OY25TocjlcOBhUxbYk/0I70Wi9ZrtxUGZIOI627UywrV5cEy4xX\nO0u2YUB8nTUg3uni0ksvBTBlQHzMsz+nX716NV796lcDGLMiANixYwcAYP/+/Un7tNqdxkumZaIW\nDSVG7plrvxKQ8uTW6pe5ujR2xGVrMZhBSEP3Sksl67IlV7Ymb9sHmLrXrpZ+zfxS22iXEKX7lO4j\nHlR4P3hedsXb77DbnQecEydOLGy/s2LFikV5eVdUySYNrJNTjZMkl7etGF5C/Kmb2KYuJn4pnQR/\ngdXhcPSKwTCh5uipYQNWkcxSdup6Kq92Jkilsy7HtPQ6ZYN1htUypGa+3OdX4tcxmPHw9Tgglbfj\nOXz4MIAx+2ERe9++fYvS1jCeJrpkTJr2j69JzzpGacknpalZCeTKzfWPWjHemZDD4egVg2JC0la7\nzbTakd2qGWlGfC1K+bVMTZp5Sy9xttWZJFtLdeX+sr0cksHaD29MyODjI0eOLKThLZn5GutHOfst\nOo7EErWze6k/afuRlb1YnBBWHVPTZ7Vl5+BMyOFw9IrBMKGUxyg3q8zPz4sfTNe6Da0u/JR9cV4L\no2jrebOwNetMLNlqWftz3oMHDwIADh06BGDKiJjlsLeMAxCPHTt2WnAiv8YRM6EuZupanUnDOLRa\nW66MGs1F239y6UvaVipNDZwJORyOXjEYJpSCZubQxttYR+tUnI1knyW+Qzv7SXZr2InEFrXnS7Zo\nvXfs9dq9ezeAqSbEeiB7wOKXVYEpA+IyJOaZQ8q23CsSUhto2IxWn7F6dC12xvZK+VPprX1RC2dC\nDoejVwyaCZV0D0kL0WpCuTqbo77lBcJSOo0+IJWt9fo1mZw1RsTCmHJp40hdPmYdh19QZU8XR1Lz\npz2YmRw9enSBAbGe1GRHzTpy2olGA7PmqWXYqbxa/a/EpGo9bxZt0apXauFMyOFw9IpBMyGL58o6\nwlu8abm80oxg8Sa11bBS+Wtnqpi9xLDEwMTshP8ym2ENiN8D43fMuO6TJ08upGUWldvqR2IMOZQ8\nsznUPK+2MUc520q/h7juUh6LDaU0HifkcDjOKgyaCeWgGWmta/xcPk1ZtXpCCtoZzOLBipmN5KWR\nmF3qPiWvGLMZPo7f/4rLjFlPyT6t18bSF7SanYZxa+N9pLpKz0di521jeZqo/W3l4EzI4XD0ikEy\noTaeB6ksLYuxjO5x3twb5aU8bWN4YjRjX7TxTDU2aPUwBjMc/it5AWtmWW3bNY9zLFHbPzR91erF\n07JgjdfVam+J5XTFgBjOhBwOR68YDBNKxWowcgwjdS0+rz1u2mFFDZuSGE0tC0y1jdVDWBMnJHkO\ncx436bmV2FYMC0uMYX0XsYt+koOWGZVsiX8XuQ0ktbBopFY4E3I4HL1iMEwIyI+spW/m5GbS2qjO\nLta5lpnCOqtIdpdmWUkP03pnSt6xWpZVslnL5FJ5U/cRX0/ZILVBnM7S7jlYY5VKdvCx9C5cnF/z\nnLr0tAHOhBwOR88YFBNi1GgrubxSmUvhBdCs6WvjgbSzu8YuLVu0eGO095Ozv1RurddUc59tNZ9S\n/loPpwXafh/DwsStTFSLwQxCqc5myScNWDWdzLoMsP7ASnm0g49muSlRbcnekk1SwJ12yVTKJy0X\na1GqQwrszJWlbetUmrYDeimvdrLquo018OWYw+HoFYNgQkRkcsXmygD0I3xuhtAwMimvho1pGVuX\nrlQJVld46px1xtXUkWMnVmZnFcVLaSTG0exnln6RKkvDvqx1SNdT96ftm9ZlmjMhh8PRKwbBhIDx\n6Cm5EzWu1LYoCaI51My4NbNyyc4Uw9OyQ6smobErroPPx8FzuVmzFJYRl21FSduqFf5zfSD1OZRa\njbGUX9tH27BdqaxaPcmZkMPh6BXiIEREXyKiPUT0SOPceiK6n4ienPxdNzlPRPQnRLSDiH5CRG80\nGTMzsyj4kNeh/K85u1sRr2mbOpR25OY8sR2xnfPz85ifn8/a3aw3V39cV+5fnL5pk9VubZs1/1na\nD8BC2+RsyR3n7jFln/Y+LLbHz1Kbd2ZmRrQ7Pl/qN9q6tXVK0PzmpH4kQcOEvgzgpujcHQAeCCFc\nDeCByTEAvAfA1ZN/twP4vNkih8PxioKoCYUQ/oaIroxO3wzgxsn/7wLwfQAfn5z/izAeDh8korVE\ntDGEsEuqJ7Wu1cQ8WOMdpFFds66V1sKSjtClzhFrK5r8td4lDbRlaZ9LykuW08Os9lp0Pymd5tnn\n+pZG37PYWEqr1Z9SdebskF7+lVCrCW1oDCy7AWyY/H8TgF800s1Nzp0GIrqdiLYR0bauxWWHw3H2\noLV3LIQQiMg8ioQQtgLYCgDLli0LzfVkzrOgmRGkNHEdFsYkpdHO1CVvjGRvfD73AbUms9R6M6Tj\n1H1Ls7cEjQ1tJykLC9Pej9S/UuVbmX5sQymflhVqGZLmObZlooxaJvQcEW2cVLwRwJ7J+Z0ALm+k\n2zw553A4HEnUDkL3Arhl8v9bANzTOP8bNMZbAezX6EHA6evzpnegpNBbvRbadBqPVc6jI3muSmlz\nHhLJc5K6v5ydWvslG5sewFT7aZ6DZJN0Lde+JaTSS23FYO9efL30PHJppecSp889h1Jfy/WbXLoc\nLP3HCnE5RkRfxViEvpiI5gB8EsCnAHydiG4D8HMAH5wkvw/AewHsAHAEwG+aLXI4HK8o0BBE4WXL\nloXVq1cvjKLxpnZdQru212hCbWzQ6jM5aDSsXB5t2ZJuFc+kGlg0OC0kfUxTt1ZLsdoZM5WaMq2a\nXQpt+1eprBwOHTq0PYRwvWSbR0w7HI5eMZh3x6wswzILaNJrPA61dZS8HdrZLaVhxPZK9mhZoIQS\nA2rLsmoYEqfRxkyVnofEOnLpcudTLKsNq8pBerba/mVhQNbfRw7OhBwOR68YDBMC0m8cA+lR3jqy\nS6N1zsNTsiO2WzsDa+zN2dIVm+miTo1uJukhOZtS6brSVqTyusibssnaZ+P+VGKNUhu0bZuU56sr\njdSZkMPh6BWDYkIxcmv9JviaZuvl5nlpZkh5sHJ5rTN0agaLy9LOvBaWok2jvR+NDqW9v1y65nOw\nslmtBlPyYMVlWaFhcrn+JDGiNjpOjTbUJQtvwpmQw+HoFYNgQrlIyxwDas5cWq+X1SNRGuW1s0qp\nLKtWoo0VsXgscnksM1xtXq2dJSZhZYsaL5WVmcZMPGVL3Da1zFnTd7V1WZFaGTSvtYEzIYfD0SsG\nwYQALHxtr4ncd4bbeDXisnMeuWY9WoZTE/Oj9eppvrkcX28b56SZsbvSBWq8YlpmJGl5ljaT9ECL\nppI7znlbu4o3ytmXQrPsXHu37QODGYQA/Q+s5BrWPqDS4CPZU9sJNINCfE0afNq47q3iZOpHIbWF\n9Fyk85Z7qnVDlyYFLUr255ZIGntKKLn/S2mtZWvT1A6KvhxzOBy9YlBMKAfLrJ47jsvSzBjaZaB2\n9izRfsl1bWUcGiahbYuS2KktUyucl2yytkEuX8p2LQuxhgmUoA0LsDg8pLK1zLlrYbsEZ0IOh6NX\nnBVMKDWzWdfu2hG96XLVMIHmscUVa9VOcvfRpU6Wu59cuZa8FgE3rkvLEnP2aQReqxBtqVsr9Odg\nqUOrO0naXMmOrl31zoQcDkevGAwTSrEDRo5pxPmbaXPXJVjd2qm6LXpTTX0lGyzXcm0mvZCbslk7\nE8d1MEqv3WjDGOLrOXQx68foor+UNKta27TMVMO8tYzT2pedCTkcjl4xGCakYQka/SNVXo0dGmah\n9UBYPD1WnUMDqR27mO0tGk8zvYQab2WurtJsX+sdkp5XidHl0IUHMVenVs/ReEI9TsjhcJwTGAwT\nampCNZpK/AKhFW20IOl6aSau1QOk/Km0Wvtq05XSlD7HUrI5Fcsjtav1WHMftfFDNd4xrUereaz1\nhkkoPWupTWp/g86EHA5HrxgMEyqhxCByaaV0uXyl2UVab+eQYgFaj1oXGkutDqONOWleq2Vwpdie\ntlqWZEuThdfG1Vg0IQubTZ23sBWpjSyML7f64L+j0ahYVw7OhBwOR68YFBMqzYZaWJlPPBM017VW\nj4nEbpp1WVmVNCOnbOmaLZbK0npKtDN0nL5ZlpURSbZpYmG0jCGV3spOYls0Wp3EKHPQPr8QwmmM\n57zzzgMALF++HACwatUqAMDs7CwA4Pnnny/WzXAm5HA4esVgmFDJi5Aa3aX1tbR9kLQun5mZWRj5\nT506VbQnLiNOp4lLkWa93GdEtTpCyR7JO2PRxGo8UU2k8lnuMVVGDimWpS1L20Yl+63aVhsPrpWp\nptJxWmZAzHw2btwIANi8eTMAYMWKFQCAhx9+WGWjMyGHw9ErBsOEShpGyQvQzN/82zbOI4Sg+vqi\nZGfqOGWLNLvF71blmFwbe2p0HKtWIuWTbE2VYa0jlV7LbiWU2kHrEZT6S6qcGvZXOp+yn/sgM6HV\nq1cDAF7zmtcAAK655hoAwCWXXAIAuOuuu4q2LJQrJSCiLxHRHiJ6pHHuPxPR3xPRT4jofxLR2sa1\nO4loBxE9QUS/qrLC4XC8YqFhQl8G8F8A/EXj3P0A7gwhnCSiTwO4E8DHieh1AD4E4B8CuAzAXxPR\nPwghnIIAzdo5FZeSi9uI82qhib3InZeigjVreasHKNbASl4zK/vQMFMt07RoD/Gx9pnmmIaGJUht\no+0DNWUz2mhfWt1J65WNzze1yGXLxsPG+vXrAUwZ0LXXXgsAWLlyZbLMHEQmFEL4GwB7o3N/FUI4\nOTl8EMDmyf9vBnB3COHlEMLPAOwA8GaTRQ6H4xWFLjShjwL42uT/mzAelBhzk3OngYhuB3D75P8m\nxb8Jq/qfQyldm3V0E01dp0YDSaXjcjTbYEtla2NkUnlqtTcLallX7nqJbWkZkUYL66IPau3XPsO2\nzBWYMiLWgLZs2bLovBatBiEi+gSAkwC+Ys0bQtgKYCsAjEaj+p7pcDjOalQPQkR0K4D3AXhXmA6T\nOwFc3ki2eXKutg4A6dFbq1vE6ePrVg1Gm6Zki6a+Nh4rTi8xOKkNJK9MGwZrYYJWlmud/S0MT2K/\npb4q1dF2NZAqQ6th5Zh0qi+cPDlWYk6cOAEA2Llz/BNnXZIjprWoGoSI6CYAfwDgn4YQjjQu3Qvg\nfxDRZzAWpq8G8H8rygdQFi9znw2QflDSgy91hC46ibXMWkE0teSzCM7adFrhvHbQ1bRdF0sLyQ5t\nOsvStca+VJ3N9PE5zTJdaxOn4cFnz549AIDt27cDAC677DIA02BFLcRBiIi+CuBGABcT0RyAT2Ls\nDTsfwP0TYx8MIfybEMKjRPR1AI9hvEz7raDwjDkcjlcuqI1Q2BVGo1EojZ6pUbntjGqh+FaRW7Mc\n0C7p2iz9tOKqZZki1SHBeh+pZ60Vv7XPWLMElOpK2c3HtcvHXJkpG61MNIdSHfx/foGVX9vgoMW1\na8fhgrwc+8EPfrA9hHB9sUL4axsOh6NnDOa1DY2QKp2Lyysda8rpyr1ZSq8V0KXrKW1Lywol0T5O\nV9IgtBpRDppn3VZHi8spMQrtfWiZU7NMre4UH5eeda6uNmw3TssC9JEjYzmYX/A+fPgwgOlrHVo4\nE3I4HL1iMExI47Fopq1xWTev52b7Ur21s01NHj4vfZLEomVpjy1oq5dJ51MMWbK/jfdJYh9tGFCu\nrtp+ltKdcpDst2ioMSM6duwYgKnXzNomzoQcDkevGAwTAnQaRNsyc8epF0CtnpwaRmHRwdrW0SWT\n4/RW7Uerh5SgZSVaZqR51lpNJZWu1uulZYClPFp7NVpdXEf8m8l9eE+CMyGHw9ErBsOErGxHm76W\nBVjW2VLIuwVaBhGnT53PeXqszK10Pb73XF1dtElO+5F0Nc39WXSjUh2lmKNaXUnTd9t6VTXsK9ff\n47TWjwE6E3I4HL1iEBHTRPQ8gMMAXujblgwuxjBtc7vsGKptQ7ULqLft1SGEV0mJBjEIAQARbdOE\nePeBodrmdtkxVNuGahew9Lb5cszhcPQKH4QcDkevGNIgtLVvAwoYqm1ulx1DtW2odgFLbNtgNCGH\nw/HKxJCYkMPheAXCByGHw9ErBjEIEdFNNN6xdQcR3dGjHZcT0feI6DEiepSIPjY5v56I7ieiJyd/\n1/Vk34iIfkxE354cbyGihybt9jUisn1hvDu71hLRN2i8K+/jRPS2IbQZEf3e5Dk+QkRfJaLlfbUZ\npXcyTrYRjfEnExt/QkRvPMN2ndEdlnsfhIhoBOBPAbwHwOsAfJjGO7n2gZMAfj+E8DoAbwXwWxNb\n7gDwQAjhagAPTI77wMcAPN44/jSAz4YQfgnASwBu68Uq4HMAvhNCeC2AX8bYxl7bjIg2AfgdANeH\nEK4DMMJ4d+C+2uzLAG6KzuXa6D0YbxJxNcZ7833+DNt1P4DrQgj/CMBPMf6mPGjxDss3Afivk99v\nO/A7IX39A/A2AN9tHN+J8RbTQ7DtHgDvBvAEgI2TcxsBPNGDLZsx7qjvBPBtAIRxFOuyVDueQbsu\nBPAzTJwcjfO9thnGm27+AsB6jN+R/DaAX+2zzQBcCeARqY0A/DcAH06lOxN2Rdf+BYCvTP6/6LcJ\n4LsA3ta2/t6ZEKadhZHdtfVMgoiuBPAGAA8B2BBC2DW5tBvAhh5M+mOMt1nitwMvArAvTLfj7qvd\ntgB4HsCfT5aKXyCiVei5zUIIOwH8EYBnAOwCsB/AdgyjzRi5NhrSb+KjAP735P9LYtcQBqHBgYgu\nAPBNAL8bQjjQvBbGU8AZjWsgovcB2BNC2H4m61ViGYA3Avh8COENGL8DuGjp1VObrQNwM8aD5GUA\nVuH0Zcdg0EcbSaAWOyxbMIRBqNNdW9uCiM7DeAD6SgjhW5PTzxHRxsn1jQD2nGGzbgDwfiJ6GsDd\nGC/JPgdgLRHx51j6arc5AHMhhIcmx9/AeFDqu81+BcDPQgjPhxBOAPgWxu04hDZj5Nqo998ETXdY\n/shkgFwyu4YwCP0QwNUTr8UsxsLXvX0YQuMPonwRwOMhhM80Lt0L4JbJ/2/BWCs6Ywgh3BlC2BxC\nuBLj9vk/IYSPAPgegA/0ZdfEtt0AfkFE10xOvQvjzS97bTOMl2FvJaKVk+fKdvXeZg3k2uheAL8x\n8ZK9FcD+xrJtyUHTHZbfH07fYflDRHQ+EW1B5Q7Lp+FMiXKCMPZejFX4/wfgEz3a8XaMKfFPAPzt\n5N97MdZfHgDwJIC/BrC+RxtvBPDtyf+vmnSCHQD+EsD5Pdn0jwFsm7Tb/wKwbghtBuAPAfw9gEcA\n/HeMdw3upc0AfBVjbeoExuzxtlwbYex0+NPJ7+HvMPbwnUm7dmCs/fBv4M8a6T8xsesJAO/pwgZ/\nbcPhcPSKISzHHA7HKxg+CDkcjl7hg5DD4egVPgg5HI5e4YOQw+HoFT4IORyOXuGDkMPh6BX/HzZ5\nGwJi8vDjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa6ccc94e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa3a4bcc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BFwCLJC0spcTJfYMiYp4ahdluukmIzwLnSToR+H/ARcA48ADwUaqW5g3Afd0GGXNXO+83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\nhDvgGg0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa3a41f780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform a sanity check on some random training samples\n",
    "ix = random.randint(0, len(preds_train_t))\n",
    "imshow(X_train[ix])\n",
    "plt.show()\n",
    "imshow(np.squeeze(Y_train[ix]))# 128*128*1\n",
    "plt.show()\n",
    "imshow(np.squeeze(preds_train_t[ix])) # 128*128*1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HLK9xiEq3GF0OYFJyojRBWRzr5zMI0UUfNSkcJfx2zHGcXhmMEmrOqpE2Icq2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\nP0qquBTtLZXn6ufKpPZJy9b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2hxAenix/DeNBqe9j9rMAng0hvBhCOAPg6xgfxyEcswh3\njHr/m6DpF5Z/aTJAzsyuIQxCjwC4YRK1WImx4+ubfRhC4xehfA7AUyGEP2hs+iaAuyf/vxtjX9F5\nI4RwXwjh6hDCNoyPz/8NIfwSgIcA/EJfdk1sOwTgeSJ6x2TVnRh//LLXY4bxbdhtRLR2cl6jXb0f\nswbcMfomgH8xiZLdBuB447Zt5tD0C8sfCku/sPxRIlpFRNth/MIyy/lyygmOsQ9g7IXfDeB3erTj\nH2MsiX8I4LHJvw9g7H95EMCPAfwdgM092vg+AN+a/P9tk4vgGQB/AWBVTzb9IwA7J8ftfwG4dAjH\nDMC/B/A0gB8B+O8YfzW4l2MG4MsY+6bOYKwe7+GOEcZBhz+e/D08jnGE73za9QzGvp/4N/AnjfK/\nM7FrF4D3d2GDP7bhOE6vDOF2zHGcNzE+CDmO0ys+CDmO0ys+CDmO0ys+CDmO0ys+CDmO0ys+CDmO\n0yv/Hw8fn1I60HtxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd3918de48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc78614e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IaEhCjIiopEKMiIDae4/blYQYEf2RhBgRkecyR0S8VxJiRERFHvyMmIQYEb2XTpWIiMNy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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc784c16a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform a sanity check on some random validation samples\n",
    "ix = random.randint(0, len(preds_val_t))\n",
    "imshow(X_train[int(X_train.shape[0]*0.9):][ix])\n",
    "plt.show()\n",
    "imshow(np.squeeze(Y_train[int(Y_train.shape[0]*0.9):][ix]))\n",
    "plt.show()\n",
    "imshow(np.squeeze(preds_val_t[ix]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encode and submit our results\n",
    "\n",
    "    Run-Length Encoding（RLE）行程长度的原理是将一扫描行中的颜色值相同的相邻像素用一个计数值和那些像素的颜色值来代替。例如:aaabccccccddeee，则可用3a1b6c2d3e来代替。对于拥有大面积，相同颜色区域的图像，用RLE压缩方法非常有效。由RLE原理派生出许多具体行程压缩方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run-length encoding stolen from https://www.kaggle.com/rakhlin/fast-run-length-encoding-python\n",
    "def rle_encoding(x):\n",
    "    dots = np.where(x.T.flatten() == 1)[0]\n",
    "    run_lengths = []\n",
    "    prev = -2\n",
    "    for b in dots:\n",
    "        if (b>prev+1): run_lengths.extend((b + 1, 0))\n",
    "        run_lengths[-1] += 1\n",
    "        prev = b\n",
    "    return run_lengths\n",
    "\n",
    "def prob_to_rles(x, cutoff=0.5):\n",
    "    lab_img = label(x > cutoff)\n",
    "    for i in range(1, lab_img.max() + 1):\n",
    "        yield rle_encoding(lab_img == i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_test_ids = []\n",
    "rles = []\n",
    "for n, id_ in enumerate(test_ids):\n",
    "    rle = list(prob_to_rles(preds_test_upsampled[n]))\n",
    "    rles.extend(rle)\n",
    "    new_test_ids.extend([id_] * len(rle))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create submission DataFrame\n",
    "sub = pd.DataFrame()\n",
    "sub['ImageId'] = new_test_ids\n",
    "sub['EncodedPixels'] = pd.Series(rles).apply(lambda x: ' '.join(str(y) for y in x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub.to_csv('sub-dsbowl2018-1.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}